Different from those model-agnostic approaches, previous works in this line aim to train the generation model to learn how to cooperate with the retrieval model. It is also worth noting that most works in this line adopt the sentence-level retrieval, when integrating the retrieval information in the training process. To achieve its goal, [1]} and [2]} propose a data augmentation method to integrate the retrieved information, where \(x\)  is concatenated with \(y^r\)  before feeding into the model . Following the data augmentation approach, [3]} propose more matching methods to determine including which retrieved example in the source is better.
